CN107706923A - A kind of load active management method suitable for active distribution network - Google Patents
A kind of load active management method suitable for active distribution network Download PDFInfo
- Publication number
- CN107706923A CN107706923A CN201711019662.3A CN201711019662A CN107706923A CN 107706923 A CN107706923 A CN 107706923A CN 201711019662 A CN201711019662 A CN 201711019662A CN 107706923 A CN107706923 A CN 107706923A
- Authority
- CN
- China
- Prior art keywords
- load
- peak
- distribution network
- active
- response
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of load active management method suitable for active distribution network, this method, which is established, has carried out all types of user to the changing load amount model based on electricity price incentive mechanism, and in this, as the main constraints of workload demand side response；In addition, consider more optimization aims such as power distribution network operation grade, users'comfort, network load characteristic, and consider the constraint such as workload demand characteristic, load responding characteristic, load operation mode, power supply status, Spot Price, Demand Side Response process is solved using NSGA II algorithms.Beneficial effect：The method simple practical that is carried of the present invention, and the economic benefit in distribution region can be improved by this method, the grid-connected influence to system of high permeability distributed energy can be effectively improved.
Description
Technical field
The present invention relates to grid side demand management field, more particularly to a kind of load suitable for active distribution network actively to manage
Reason method.
Background technology
Demand Side Response is referred to by price signal and incentive mechanism come the effect of side in the market of increasing demand, in electricity
Peak-valley TOU power price utilizes different in different time sections electricity price in the industry of Lixing, so as to guide user to change the consumption habit of oneself,
The response of Demand-side is formed, reduces burden of the peak load reduction to power system.
Active distribution network refers to that inside is connected to distributed energy, has the power distribution network of control and service ability, matches somebody with somebody with tradition
Power network is different, and active distribution network will be controlled by and adjust on one's own initiative to the distributed energy of inside.The environment of active distribution network
Under, may participate in Demand Side Response accesses system except traditional power consumer, such as factory, trade company, in addition to via power distribution network
System, possessed by user or to small power generation equipment such as the distributed power generation of the direct power supply of user, distributed energy storages.Therefore actively matching somebody with somebody
Demand Side Response under power grid environment is except considering electricity price and customer charge characteristic, it is also necessary to considers the load of distributed power source
Characteristic.
Find by prior art documents, the distributed power source based on NSGA-II algorithms is grouped with micro-capacitance sensor
Distribute rationally（Distributed power sources of Sheng Wanxing, Ye Xueshun, Liu Keyan, the Meng Xiaoli based on the algorithms of NSGA- II is grouped with micro-capacitance sensor
Distribute [J] Proceedings of the CSEEs, 2015,35 (18) rationally:4655-4662.[2017-08-09].DO：10.13334/
j.0258-8013.pcsee.2015.18.011）It is proposed containing the mixing collection between wind-force, photovoltaic distributed power supply and micro-capacitance sensor
Into electric power system and its Optimal Configuration Method.The document is considered in mixed power supply system, for reasonable disposition distributed electrical
Source, load and energy storage, establish the multiple-objection optimization using cost of investment, the expectation of power grid electric deficiency and network loss as object function
Model.Using non-dominant property Sorting Genetic Algorithm (the nondominated sorting genetic based on elitism strategy
Algorithm II, NSGA- II) solve distributing rationally for distributed power source in hybrid integrated electric power system, load and energy storage
Method.Although the document considers the optimization allocation of distributed power source in detail, Demand Side Response condition is not concerned with
The active management of lower distributed power source.User response behavioral study under Peak-valley TOU power price（Ruan Wenjun, Wang Beibei, Li Yang, poplar
Win user response behavioral study [J] electric power network techniques under spring Peak-valley TOU power price, 2012,36 (07):86-93. [2017-
08-09]. DOI：10.13335/j.1000-3673.pst.2012.07.006）Propose：Former based on consumer psychology
Under the Peak-valley TOU power price that reason is established on the basis of customer response model, according to Peak-valley TOU power price formulation process needs, it is based on
Weighted least-squares method establishes the identification model of user-responsiveness curve；Then it is directed to time-of-use tariffs difference performance
User, formulated the real-time simulation flow of user response behavior under Peak-valley TOU power price, and demonstrated above-mentioned model
Validity and superiority.But the document does not account for distributed power source and participates in asking for Demand Side Response as a load part
Topic.
The deficiency for more than, the present invention consider the participation of distributed power source and the load responding based on tou power price,
Object function and constraint are established, successfully utilizes NSGA-II（Non-dominant property Sorting Genetic Algorithm nondominated
sorting genetic algorithm Ⅱ）Algorithm carries out multiple-objection optimization, can improve the economic benefit in distribution region, effectively
Improve the grid-connected influence to system of high permeability distributed energy.
Therefore, prior art has yet to be improved and developed.
The content of the invention
It is an object of the invention to provide a kind of load active management technology suitable for active distribution network, the Optimized model
The economic benefit in distribution region can be improved, is effectively improved the grid-connected influence to system of high permeability distributed energy.
Technical scheme is as follows：A kind of load active management method suitable for active distribution network, it include with
Lower step：
（1）Load is classified, establishes the response curve and its ginseng of peak period to paddy period respectively to different load type
Number, the response curve of peak period to usually section and its response curve and its parameter of parameter peace period to paddy period；
（2）It is based on（1）Three kinds of response curves of middle foundation, the fitting function of load of day part is established, by obtaining real time data
With with fmincon（Linear multivariate function minimum value function）Function asks for response model data；
（3）Establish dsm optimization aim model and dsm response constraints model；
（4）More optimization aim operation functions are established, Demand Side Response process is optimized using NSGA-II algorithms.
The described load active management method suitable for active distribution network, wherein, the parameter includes dead band threshold value, satisfied
With electricity price when area's threshold value, linear zone slope, peak and during paddy is poor, peak when with usually electricity price it is poor, usually with electricity price during paddy is poor, maximum is born
The lotus rate of transform.
The described load active management method suitable for active distribution network, wherein, in step（2）In, the plan of day part
Close function of load and be divided into three sections, each section of time interval is peak period, usually section, paddy period；It is real that parameter includes tou power price
The fitting load after actual measurement load, implementation, implementation leading peak, flat, average value of the paddy period total load within the corresponding period before applying.
The described load active management method suitable for active distribution network, wherein, the dsm optimization aim
Model includes user power utilization comfort level, ADN（Active distribution network, active distribution networks）Comprehensive cost,
DG（Distributed power source, distributed generation）Year operation maintenance expense, higher level's power network power purchase are taken, network power loss
Expense, dsm cost, regenerative resource sale of electricity expense, network load peak-valley difference.
The described load active management method suitable for active distribution network, wherein, the dsm response constraint
Condition model includes node voltage constraint, power flow equation constraint, branch current constrains, outage capacity constrains, dump energy is horizontal
Constraint.
The described load active management method suitable for active distribution network, wherein, the object function includes three mesh
Mark, respectively users'comfort function are minimum, ADN（Active distribution network, active distribution networks）It is comprehensive
Running cost is minimum and power network peak-valley difference is minimum.
The described load active management method suitable for active distribution network, wherein, the step（4）It is middle to use NSGA-
II algorithms are to target and step（3）In Demand Side Response constraints model and dsm optimization aim model carry out
Optimization, can calculate energy storage in active distribution network, diesel-driven generator, charging pile regulated quantity and customer charge response quautity.
Beneficial effects of the present invention：The present invention is by classifying, then arriving the load of classification according to the peak period
The response curve and its parameter of paddy period, the response curve of peak period to usually section and its sound of parameter peace period to paddy period
Answer curve and its parameter；So the fitting function of load of day part can be established according to these three curves；Pass through fmincon letters again
Number asks for response model data；Then dsm optimization aim model and dsm response constraints mould are established
Type；More optimization aim operation functions are finally established, Demand Side Response process is optimized using NSGA-II algorithms；So as to real
Now to the load active management of active distribution network, can try to achieve energy storage in active distribution network, diesel-driven generator, charging pile regulated quantity and
Customer charge response quautity；In addition, the invention can also improve the economic benefit in distribution region, high permeability distribution is effectively improved
The grid-connected influence to system of the energy.
Brief description of the drawings
Fig. 1 is a kind of FB(flow block) of the present invention.
Fig. 2 is another flow chart of the present invention.
Embodiment
To make the objects, technical solutions and advantages of the present invention clearer, clear and definite, develop simultaneously embodiment pair referring to the drawings
The present invention is further described.
As shown in figure 1, the invention discloses a kind of load active management method suitable for active distribution network, it include with
Lower step：
（1）Load is classified, establishes the response curve and its ginseng of peak period to paddy period respectively to different load type
Number, the response curve of peak period to usually section and its response curve and its parameter of parameter peace period to paddy period；
（2）It is based on（1）Three kinds of response curves of middle foundation, the fitting function of load of day part is established, by obtaining real time data
With with fmincon（Linear multivariate function minimum value function）Function asks for response model data；
（3）Establish dsm optimization aim model and dsm response constraints model；
（4）More optimization aim operation functions are established, Demand Side Response process is optimized using NSGA-II algorithms.
Fmincon functions refer in the present invention：Linear multivariate function minimum value function；NSGA-II algorithms refer to：
Non-dominant property Sorting Genetic Algorithm, English are：nondominated sorting genetic algorithm Ⅱ.
Specifically, when the parameter includes dead band threshold value, saturation region threshold value, linear zone slope, peak and during paddy electricity price it is poor,
During peak with usually electricity price it is poor, usually with electricity price during paddy is poor, the peak load rate of transform.
Specifically, in step（2）In, the fitting function of load of day part is divided into three sections, and each section of time interval is
Peak period, usually section, paddy period；Parameter includes the actual measurement load before tou power price is implemented, the fitting load after implementation, before implementation
Peak, flat, average value of the paddy period total load within the corresponding period.
Specifically, the dsm optimization aim model includes user power utilization comfort level, ADN（Active distribution network,
active distribution networks）Comprehensive cost, DG（Distributed power source, distributed generation）Year
Operation maintenance expense, higher level's power network power purchase are taken, network power wear and tear expense, dsm cost, regenerative resource sale of electricity expense,
Network load peak-valley difference.
Specifically, the dsm response constraints model includes node voltage constraint, power flow equation constrains,
Branch current constraint, outage capacity constraint, dump energy horizontal restraint.
Specifically, the object function includes three targets, respectively users'comfort function minimum, ADN（Actively match somebody with somebody
Power network, active distribution networks）Comprehensive operation expense is minimum and power network peak-valley difference is minimum.
Specifically, the step（4）It is middle to use NSGA-II algorithms to target and step（3）In Demand Side Response
Constraints model and dsm optimization aim model optimize, and can calculate energy storage in active distribution network, diesel generation
Machine, charging pile regulated quantity and customer charge response quautity.
As shown in Fig. 2 the invention discloses a kind of load active management method suitable for active distribution network, its flow are as follows：
The first step：To load carry out trade classification, according to its part throttle characteristics be divided into industrial load, Commercial Load, agriculture load and
Resident load；
Second step：Four type loads are established with the response curve and its parameter of peak period to paddy period, including user peak period electricity price
Pp and paddy period electricity price pv difference, response curve slope, response dead band threshold value, saturation region flex pointWith
The peak load rate of transform of peak period to paddy period under electricity price between peak and valley change, establish transfer function
3rd step：Four type loads are established with the peak period to the response curve and its parameter of usually section, including user peak period electricity price
Pp and usually section electricity price pv difference, response curve slope, response dead band threshold value, saturation region flex pointWith
The peak load rate of transform of the peak period to usually section under electricity price between peak and valley change, establish transfer function
4th step：Four type loads are established with usually section to the response curve and its parameter of paddy period, including user's usually section electricity price
Pp and paddy period electricity price pv difference, response curve slope, response dead band threshold value, saturation region flex pointWith
The usually peak load rate of transform of the section to the paddy period under electricity price between peak and valley change, establish transfer function
5th step：Based on above-mentioned 3 class responsiveness curve, the fitting function of load of day part is established,、、Respectively peak
Period, usually section, paddy period, t are any time period therein；、Before respectively tou power price is implementedThe actual measurement of period is born
The fitting load of t periods after lotus, implementation；Respectively implement leading peak, flat, paddy period total load in phase
Answer the average value in the period
6th step：The actual measurement load data of correlation is obtained first, then the load responding model established of 5 steps and each before
The fitting load of period, with the majorized function fmincon in MATLAB（Linear multivariate function minimum value function）, with it is each when
The minimum object function of quadratic sum of the difference of section load, asks for the customer response model parameter based on cool load translating ratio；
7th step：Establish dsm optimization aim, including user power utilization comfort level, ADN（active distribution
Networks, active distribution network）Comprehensive operation expense, network load peak-valley difference, there is model：
Comfort level function：
It is load transfer amount,It is load total amount,Closer to 0, user power utilization mode satisfaction is got over
Height, i.e., user power utilization is influenceed fewer
ADN comprehensive cost minimum target functions：
DG（Distributed generation, distributed power source）Operation maintenance expense, ADN superior power network power purchases take, network power wear and tear expense, dsm costWith regenerative resource sale of electricity expense
1）DG operation maintenance expenses
WithRepresent to be arranged on node respectively's
Energy-storage system, wind power generating set, photovoltaic generation unit, charging pile, diesel generating set send the operation maintenance of unit quantity of electricity
Cost；Represent installation
In nodeE energy-storage systems, wind power generating set, photovoltaic generation unit, charging pile, diesel generating set is in the active of t
Contribute；For the sampling interval, 1h is taken herein.
2）Superior power network power purchase is taken
Represent total active power of the transformer station in t；Represent units of the ADN in t superior power network power purchase
Purchases strategies.
3）Network power wear and tear expense
4）Dsm cost
Represent the quantity of interruptible load point；Represent theThe load rejection amount of individual interruptible load point；ForIndividual interruptible load point interrupts the cost of compensation of unit quantity of electricity
5）Regenerative resource sale of electricity expense
For regenerative resource online unit price of power
Network load peak-valley difference model：
For peak-valley ratio,For peak-valley ratio limits value, different users can be directed to different values is set；,Respectively distribution region day net load maximum, minimum value；,,,,Respectively distribution region is contributed in the total load of t, total photovoltaic output, total blower fan,
Total energy storage is contributed, and the response of aggregate demand side is contributed
8th step：Demand Side Response constraints is established, including node voltage constraint, power flow equation constrain, branch current constrains,
Outage capacity constraint, dump energy horizontal restraint, there is model：
Node voltage constrains：
、RespectivelyVoltage at nodeLower and upper limit；For power distribution network node total number.
Power flow equation constrains：
、Respectively nodeLocate the active and idle output of power supply；、Respectively nodeThat locates is active
And load or burden without work；、Respectively nodeAnd nodeThe voltage magnitude at place；、Respectively bus admittance matrixElementReal and imaginary parts；For nodeAnd nodePhase difference of voltage.
Branch current constrains：
ForBar branch currentThe upper limit；For branch road sum
Outage capacity constrains：
ForIndividual interruptible load point maximum interruption amount
Dump energy horizontal restraint：
、、The respectively horizontal lower limit of energy-storage system dump energy, real-time dump energy water
It is flat, dump energy upper level；Represent the quantity with energy storage device in web area.
9th step：Establish multi-target optimum operation function, using NSGA-II
(genetic algorithm of the non-dominated ranking with elitism strategy) algorithm optimizes to Demand Side Response process, based on branch trade load
Response model solves to obtain user's peak load transfer amount, as optimization constraints, to be stored up so as to try to achieve in active distribution network
Energy, diesel-driven generator, charging pile regulated quantity and customer charge response quautity.
The present invention by classifying, then by the load of classification according to the response curve of peak period to paddy period and
The response curve and its parameter of its parameter, the response curve of peak period to usually section and its parameter peace period to paddy period；Institute
So that the fitting function of load of day part can be established according to these three curves；Response model number is asked for by fmincon functions again
According to；Then dsm optimization aim model and dsm response constraints model are established；Finally establish optimize more
Object run function, Demand Side Response process is optimized using NSGA-II algorithms；Active distribution network is born so as to realize
Lotus active management, energy storage in active distribution network, diesel-driven generator, charging pile regulated quantity and customer charge response quautity can be tried to achieve；Separately
Outside, the invention can also improve the economic benefit in distribution region, and it is grid-connected to system to be effectively improved high permeability distributed energy
Influence.
A kind of load active management method suitable for active distribution network provided by the present invention has been carried out in detail above
Introduce, the principle and embodiment of the present invention are set forth herein, the explanation of above example is only intended to help and managed
Solve the method and its core concept of the present invention；Meanwhile for those of ordinary skill in the art, according to the thought of the present invention,
There will be changes in embodiment and application, in summary, this specification content should not be construed as to this hair
Bright limitation.
Claims (7)
- A kind of 1. load active management method suitable for active distribution network, it is characterised in that comprise the following steps：（1）Load is classified, establishes the response curve and its ginseng of peak period to paddy period respectively to different load type Number, the response curve of peak period to usually section and its response curve and its parameter of parameter peace period to paddy period；（2）It is based on（1）Three kinds of response curves of middle foundation, the fitting function of load of day part is established, by obtaining real time data With with fmincon（Linear multivariate function minimum value function）Function asks for response model data；（3）Establish dsm optimization aim model and dsm response constraints model；（4）More optimization aim operation functions are established, Demand Side Response process is optimized using NSGA-II algorithms.
- 2. the load active management method according to claim 1 suitable for active distribution network, it is characterised in that the ginseng Electricity price is poor when number includes dead band threshold value, saturation region threshold value, linear zone slope, peak and during paddy, peak when and usually electricity price it is poor, usually with Electricity price is poor during paddy, the peak load rate of transform.
- 3. the load active management method according to claim 1 suitable for active distribution network, it is characterised in that in step （2）In, the fitting function of load of day part is divided into three sections, and each section of time interval is peak period, usually section, paddy period；Ginseng Number includes the actual measurement load before tou power price is implemented, the fitting load after implementation, implements leading peak, flat, paddy period total load in phase Answer the average value in the period.
- 4. the load active management method according to claim 1 suitable for active distribution network, it is characterised in that the need Side management optimization aim model is asked to include user power utilization comfort level, ADN（Comprehensive cost, DG operation maintenance expense, the purchase of higher level's power network The electricity charge, network power wear and tear expense, dsm cost, regenerative resource sale of electricity expense, network load peak-valley difference.
- 5. the load active management method according to claim 1 suitable for active distribution network, it is characterised in that the need Side management response constraints model is asked to include node voltage constraint, power flow equation constraint, branch current constraint, outage capacity about Beam, dump energy horizontal restraint.
- 6. the load active management method according to claim 1 suitable for active distribution network, it is characterised in that the mesh Scalar functions include three targets, respectively users'comfort function minimum, ADN comprehensive operations expense minimum and power network peak-valley difference most It is small.
- 7. the load active management method according to claim 6 suitable for active distribution network, it is characterised in that the step Suddenly（4）It is middle to use NSGA-II algorithms to target and step（3）In Demand Side Response constraints model and dsm Optimization aim model optimizes, and can calculate energy storage in active distribution network, diesel-driven generator, charging pile regulated quantity and customer charge Response quautity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711019662.3A CN107706923A (en) | 2017-10-27 | 2017-10-27 | A kind of load active management method suitable for active distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711019662.3A CN107706923A (en) | 2017-10-27 | 2017-10-27 | A kind of load active management method suitable for active distribution network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107706923A true CN107706923A (en) | 2018-02-16 |
Family
ID=61181566
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711019662.3A Pending CN107706923A (en) | 2017-10-27 | 2017-10-27 | A kind of load active management method suitable for active distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107706923A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108646552A (en) * | 2018-04-16 | 2018-10-12 | 杭州电子科技大学信息工程学院 | The Multipurpose Optimal Method of Distribution of Natural formula energy unit parameter based on genetic algorithm |
| CN109214593A (en) * | 2018-10-19 | 2019-01-15 | 天津大学 | A kind of active distribution network power supply capacity multi-objective assessment method |
| CN109359861A (en) * | 2018-10-16 | 2019-02-19 | 国网浙江省电力有限公司经济技术研究院 | A kind of comprehensive energy intelligence instrument and its Demand Side Response method |
| CN109494727A (en) * | 2018-11-30 | 2019-03-19 | 国网江西省电力有限公司经济技术研究院 | Consider the active and idle coordination optimization operation method of power distribution network of demand response |
| CN111404146A (en) * | 2020-03-19 | 2020-07-10 | 南方电网科学研究院有限责任公司 | Power distribution method, system and terminal based on user load transfer comfort level |
| CN112507530A (en) * | 2020-11-24 | 2021-03-16 | 云南电网有限责任公司 | Distributed power flow optimization method for high-medium voltage distribution network comprising discrete equipment |
| CN112510683A (en) * | 2020-11-13 | 2021-03-16 | 安徽电力交易中心有限公司 | Incremental power distribution network flexible resource allocation method considering source load uncertainty |
| CN113507113A (en) * | 2021-06-28 | 2021-10-15 | 东北电力大学 | Light storage system control strategy based on electricity price driving |
| CN113937820A (en) * | 2021-09-03 | 2022-01-14 | 广东电网有限责任公司 | Active power distribution network optimal scheduling method based on deep learning |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106410847A (en) * | 2016-10-20 | 2017-02-15 | 中国电力科学研究院 | Regional regulation and control interaction terminal and method for distributed photovoltaic |
-
2017
- 2017-10-27 CN CN201711019662.3A patent/CN107706923A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106410847A (en) * | 2016-10-20 | 2017-02-15 | 中国电力科学研究院 | Regional regulation and control interaction terminal and method for distributed photovoltaic |
Non-Patent Citations (1)
| Title |
|---|
| 罗运虎等: "峰谷分时电价用户响应模型参数的最小二乘估计", 《华东电力》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108646552A (en) * | 2018-04-16 | 2018-10-12 | 杭州电子科技大学信息工程学院 | The Multipurpose Optimal Method of Distribution of Natural formula energy unit parameter based on genetic algorithm |
| CN108646552B (en) * | 2018-04-16 | 2020-12-11 | 杭州电子科技大学信息工程学院 | Multi-objective optimization method for natural gas distributed energy unit parameters based on genetic algorithm |
| CN109359861A (en) * | 2018-10-16 | 2019-02-19 | 国网浙江省电力有限公司经济技术研究院 | A kind of comprehensive energy intelligence instrument and its Demand Side Response method |
| CN109214593A (en) * | 2018-10-19 | 2019-01-15 | 天津大学 | A kind of active distribution network power supply capacity multi-objective assessment method |
| CN109214593B (en) * | 2018-10-19 | 2023-12-08 | 天津大学 | Multi-objective evaluation method for power supply capacity of active power distribution network |
| CN109494727A (en) * | 2018-11-30 | 2019-03-19 | 国网江西省电力有限公司经济技术研究院 | Consider the active and idle coordination optimization operation method of power distribution network of demand response |
| CN109494727B (en) * | 2018-11-30 | 2021-12-10 | 国网江西省电力有限公司经济技术研究院 | Power distribution network active and reactive power coordinated optimization operation method considering demand response |
| CN111404146B (en) * | 2020-03-19 | 2021-08-13 | 南方电网科学研究院有限责任公司 | Power distribution method, system, terminal and medium based on user load transfer comfort |
| CN111404146A (en) * | 2020-03-19 | 2020-07-10 | 南方电网科学研究院有限责任公司 | Power distribution method, system and terminal based on user load transfer comfort level |
| CN112510683A (en) * | 2020-11-13 | 2021-03-16 | 安徽电力交易中心有限公司 | Incremental power distribution network flexible resource allocation method considering source load uncertainty |
| CN112510683B (en) * | 2020-11-13 | 2023-10-13 | 安徽电力交易中心有限公司 | Incremental power distribution network flexible resource allocation method considering source load uncertainty |
| CN112507530A (en) * | 2020-11-24 | 2021-03-16 | 云南电网有限责任公司 | Distributed power flow optimization method for high-medium voltage distribution network comprising discrete equipment |
| CN112507530B (en) * | 2020-11-24 | 2022-11-08 | 云南电网有限责任公司 | Distributed power flow optimization method for high-medium voltage distribution network comprising discrete equipment |
| CN113507113A (en) * | 2021-06-28 | 2021-10-15 | 东北电力大学 | Light storage system control strategy based on electricity price driving |
| CN113507113B (en) * | 2021-06-28 | 2022-09-30 | 东北电力大学 | Light storage system control strategy based on electricity price driving |
| CN113937820A (en) * | 2021-09-03 | 2022-01-14 | 广东电网有限责任公司 | Active power distribution network optimal scheduling method based on deep learning |
| CN113937820B (en) * | 2021-09-03 | 2023-07-21 | 广东电网有限责任公司 | Active power distribution network optimal scheduling method based on deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107706923A (en) | A kind of load active management method suitable for active distribution network | |
| Kasaei et al. | Optimal management of renewable energy sources by virtual power plant | |
| CN105591406B (en) | A kind of optimized algorithm of the microgrid energy management system based on non-cooperative game | |
| CN107958300A (en) | A kind of more microgrid interconnected operation coordinated scheduling optimization methods for considering interactive response | |
| CN105990838B (en) | A kind of island microgrid Optimization Scheduling of meter and schedulable and translatable load | |
| Li et al. | Hybrid time-scale energy optimal scheduling strategy for integrated energy system with bilateral interaction with supply and demand | |
| Lu et al. | A multi-objective optimization model considering users' satisfaction and multi-type demand response in dynamic electricity price | |
| CN107316125A (en) | A kind of active distribution network economical operation evaluation method based on economical operation domain | |
| Jia et al. | Joint operation and transaction mode of rural multi microgrid and distribution network | |
| Gazijahani et al. | A new point estimate method for stochastic optimal operation of smart distribution systems considering demand response programs | |
| Elkazaz et al. | Optimization based real-time home energy management in the presence of renewable energy and battery energy storage | |
| CN105576684B (en) | A kind of electric vehicle Optimization Scheduling in the micro-capacitance sensor of photoelectricity containing high permeability | |
| Li et al. | Multi-objective optimal operation of hybrid AC/DC microgrid considering source-network-load coordination | |
| Arab et al. | Suitable various-goal energy management system for smart home based on photovoltaic generator and electric vehicles | |
| CN115511658A (en) | Building energy optimization method considering breakage of energy storage device | |
| Yuanyuan et al. | Optimization scheduling method of power grid energy-saving based on fuzzy decision | |
| CN116402307A (en) | Power grid planning capacity analysis method considering operation characteristics of schedulable flexible resources | |
| CN115912419A (en) | Heat storage-electricity storage cooperative scheduling method based on load aggregation quotient | |
| CN105514986A (en) | DER user bidding grid-connection method based on virtual power plant technology | |
| Liu et al. | Hierarchical optimal dispatching strategy for microgrid system considering user-side resources | |
| Tong et al. | Economical optimization for multi-microgrid based on Stackelberg game | |
| Wang et al. | Study on the economic dispatch of regional integrated energy system based on master-slave game | |
| Zheng et al. | Research on optimal operation strategy of distributed energy based on distribution network planning | |
| Xu et al. | Generation and Load Integrated Optimal Scheduling Incorporating Distributed Energy Storage and Adjustable Load | |
| Nan et al. | Optimal Configuration of Energy Storage Capacity considering Generalized Energy Storage Resource Dispatching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180216 |